






"""simple docstring"""





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      List[str]     ,		__snake_case  :      NestedDataStructureLike[PathLike]     ,		__snake_case  :      Optional[NamedSplit] = None     ,		__snake_case  :      Optional[Features] = None     ,		__snake_case  :      str = None     ,		__snake_case  :      bool = False     ,		__snake_case  :      bool = False     ,		__snake_case  :      Optional[str] = None     ,		__snake_case  :      Optional[int] = None     ,		**__snake_case  :      Tuple     ,		)			->							Any:
											super().__init__(
											    __snake_case     ,		split=__snake_case     ,		features=__snake_case     ,		cache_dir=__snake_case     ,		keep_in_memory=__snake_case     ,		streaming=__snake_case     ,		num_proc=__snake_case     ,		**__snake_case     ,		)
											__magic_name__:	Union[str, Any]							=       field
											__magic_name__:	Optional[int]							=       path_or_paths if isinstance(__snake_case     ,		__snake_case					) else {self.split: path_or_paths}
											__magic_name__:	Optional[int]							=       Json(
											    cache_dir=__snake_case     ,		data_files=__snake_case     ,		features=__snake_case     ,		field=__snake_case     ,		**__snake_case     ,		)






					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Any:
											# Build iterable dataset
											if self.streaming:
																	__magic_name__:	List[str]							=       self.builder.as_streaming_dataset(split=self.split					)
											# Build regular (map-style) dataset
											else:
																	__magic_name__:	int							=       None
																	__magic_name__:	Optional[int]							=       None
																	__magic_name__:	Union[str, Any]							=       None
																	__magic_name__:	Optional[Any]							=       None

																	self.builder.download_and_prepare(
																	    download_config=__snake_case     ,		download_mode=__snake_case     ,		verification_mode=__snake_case     ,		base_path=__snake_case     ,		num_proc=self.num_proc     ,		)
																	__magic_name__:	List[str]							=       self.builder.as_dataset(
																	    split=self.split     ,		verification_mode=__snake_case     ,		in_memory=self.keep_in_memory					)
											return dataset



class       __A		:

					def __init__(			self  :      List[str]     ,		__snake_case  :      Dataset     ,		__snake_case  :      Union[PathLike, BinaryIO]     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[int] = None     ,		**__snake_case  :      Dict     ,		)			->							Dict:
											if num_proc is not None and num_proc <= 0:
																	raise ValueError(F'num_proc {num_proc} must be an integer > 0.'					)

											__magic_name__:	Dict							=       dataset
											__magic_name__:	int							=       path_or_buf
											__magic_name__:	Optional[int]							=       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											__magic_name__:	Any							=       num_proc
											__magic_name__:	Dict							=       """utf-8"""
											__magic_name__:	List[Any]							=       to_json_kwargs

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							int:
											__magic_name__:	Union[str, Any]							=       self.to_json_kwargs.pop("""path_or_buf"""     ,		__snake_case					)
											__magic_name__:	str							=       self.to_json_kwargs.pop("""orient"""     ,		"""records"""					)
											__magic_name__:	Dict							=       self.to_json_kwargs.pop("""lines"""     ,		True if orient == """records""" else False					)
											__magic_name__:	List[str]							=       self.to_json_kwargs.pop("""index"""     ,		False if orient in ["""split""", """table"""] else True					)
											__magic_name__:	List[Any]							=       self.to_json_kwargs.pop("""compression"""     ,		__snake_case					)

											if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																	raise NotImplementedError(F'`datasets` currently does not support {compression} compression'					)

											if isinstance(self.path_or_buf     ,		(str, bytes, os.PathLike)					):
																	with fsspec.open(self.path_or_buf     ,		"""wb"""     ,		compression=__snake_case					) as buffer:
																							__magic_name__:	Any							=       self._write(file_obj=__snake_case     ,		orient=__snake_case     ,		lines=__snake_case     ,		index=__snake_case     ,		**self.to_json_kwargs					)
											else:
																	if compression:
																							raise NotImplementedError(
																							    F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
																							    """ was passed. Please provide a local path instead."""					)
																	__magic_name__:	Tuple							=       self._write(
																	    file_obj=self.path_or_buf     ,		orient=__snake_case     ,		lines=__snake_case     ,		index=__snake_case     ,		**self.to_json_kwargs					)
											return written

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      List[Any]					)			->							Any:
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Union[str, Any]							=       args

											__magic_name__:	Tuple							=       query_table(
											    table=self.dataset.data     ,		key=slice(__snake_case     ,		offset + self.batch_size					)     ,		indices=self.dataset._indices     ,		)
											__magic_name__:	int							=       batch.to_pandas().to_json(
											    path_or_buf=__snake_case     ,		orient=__snake_case     ,		lines=__snake_case     ,		index=__snake_case     ,		**__snake_case					)
											if not json_str.endswith("""\n"""					):
																	json_str += "\n"
											return json_str.encode(self.encoding					)






					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      BinaryIO     ,		__snake_case  :      Any     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      Union[str, Any]     ,		**__snake_case  :      Optional[Any]     ,		)			->							int:
											__magic_name__:	List[str]							=       0

											if self.num_proc is None or self.num_proc == 1:
																	for offset in logging.tqdm(
																	    range(0     ,		len(self.dataset					)     ,		self.batch_size					)     ,		unit="""ba"""     ,		disable=not logging.is_progress_bar_enabled()     ,		desc="""Creating json from Arrow format"""     ,		):
																							__magic_name__:	Dict							=       self._batch_json((offset, orient, lines, index, to_json_kwargs)					)
																							written += file_obj.write(__snake_case					)
											else:
																	__magic_name__,			__magic_name__:	List[str]							=       len(self.dataset					), self.batch_size
																	with multiprocessing.Pool(self.num_proc					) as pool:
																							for json_str in logging.tqdm(
																							    pool.imap(
																							        self._batch_json     ,		[(offset, orient, lines, index, to_json_kwargs) for offset in range(0     ,		__snake_case     ,		__snake_case					)]     ,		)     ,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,		unit="""ba"""     ,		disable=not logging.is_progress_bar_enabled()     ,		desc="""Creating json from Arrow format"""     ,		):
																													written += file_obj.write(__snake_case					)

											return written





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase    :   List[str]						=			{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			[
		    'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'GPTBigCodeForSequenceClassification',
		    'GPTBigCodeForTokenClassification',
		    'GPTBigCodeForCausalLM',
		    'GPTBigCodeModel',
		    'GPTBigCodePreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_bigcode import (
				    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTBigCodeForCausalLM,
				    GPTBigCodeForSequenceClassification,
				    GPTBigCodeForTokenClassification,
				    GPTBigCodeModel,
				    GPTBigCodePreTrainedModel,
				)


else:
		import sys

		lowercase    :   Tuple						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)